23. Machine-Learning-Guided Morphology Engineering of Nanoscale Metal-Organic Frameworks
Peican Chen, Zeyu Tang, Zhongming Zeng, Xuefu Hu, Liangping Xiao, Yi Liu, Xudong Qian, Chunyu Deng, Ruiyun Huang, Jingzheng Zhang, Yilong Bi, Rongkun Lin, Yang Zhou, Honggang Liao, Da Zhou, Cheng Wang, Wenbin Lin,Matter,
Volume 2, Issue 6,
We studied nanoscale metal-organic frameworks (nMOFs) from Hf-oxo clusters and linear dicarboxylate ligands with the aid of machine-learning methods for data analysis. Ligand solubility and modulator concentration were found to quantitatively predict the growth of nMOFs with a specific morphology. we use epitaxy growth sequences to design nMOFs of desirable nanostructures with enhanced substrate transport and, hence, increased activities for catalytic olefin hydrogenation. This work provides guidance for morphology engineering of nMOFs and other nanomaterials in using machine learning.